George F. Estabrook · Cambridge University Press eBooks · 2011
Fundamental to observing phenomena, hypothesizing explanations, and arguing their differential credibility is the recognition and quantification of distinctions. Are things different? If so, how different are they? The question, Can we distinguish by their weight the two groups of fish caught on different dry flies?, is an example. We have discussed several test statistics relevant to this question. Now we will examine an approach used by classical statistics to address the same basic question. This approach to hypothesis formation is widely used in the published literature of every natural science, and is not uncommon in the more quantitative publications of social science as well. It will be important for you to understand it, and possibly even use it, in your own work.
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Sebastian J. Lehmann (824223), Hansjörg Scherberger (824224) · Figshare · 2015
<p>A. Percentage of all LFP sites (both animals, N = 246) that have a significant coefficient for grip type in the various frequency bands (slow, beta, low gamma, and high gamma band) and task epochs B. Percentage of sites with a significant spatial position coefficient. C. White bars indicate percentage of sites with significant spatial coefficients averaged across bands and animals for the different epochs fixation, cue, plan, and movement. Colored lines indicate fraction of sites with spatial coefficients for target (green), gaze (blue), and for both (red). D. Directional tuning of target modulated sites (all bands) for the task epochs fixation, cue, plan, and movement, as revealed by the linear fit. Tuning directions are derived from the target coefficient vectors (green: target modulated; red: target and gaze modulated). E. Directional tuning of gaze modulated sites (blue: gaze modulated; red: target and gaze modulated). F. Scatter plots of spatially tuned sites illustrating angular orientation difference (y-axis) between target (green) and gaze position vectors (blue) against the length contrast (LC) of these vectors (x-axis). Sites with significant target and gaze modulation (red) were considered retinotopic if the coefficient vectors were of comparable length (| LC | < 0.33) and oriented in nearly opposite direction (angular difference < 135deg), as indicated by the black rectangles.</p>
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Krijnen, Wim · Data Archiving and Networked Services (DANS) · 2015
Wat is het lineare model? Hoe kunnen we dit model schatten en wat komt daar allemaal bij kijken? Hoe kun je aan model selectie doen?
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Alba Maestro-Gonzalez (4821819), M. Cruz Bilbao-Leon (4821816), David Zuazua-Rico (4821810) et al. · Figshare · 2018
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Author(s) Hung Chen, Jyh-jen Horng Shiau · 2014
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Institute of Mathematical Statistics is collaborating with JSTOR to digitize, preserve and extend access to The
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Krijnen, W.P. · Data Archiving and Networked Services (DANS) · 2015
Wat is het lineare model? Hoe kunnen we dit model schatten en wat komt daar allemaal bij kijken? Hoe kun je aan model selectie doen?
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P. Sprent · Encyclopedia of Statistical Sciences · 2004
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Peter Sprent · Encyclopedia of Statistical Sciences · 2005
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Dawn Hawkins · Oxford University Press eBooks · 2019
This chapter reviews the generalized linear model (GLZM), which is an extremely useful and increasingly popular framework approach to analysing data. Since it relies on making assumptions about the distribution of data, it is parametric. In particular, the chapter looks at the general linear model (GLM), a sub-framework of the generalized linear model which uses the normal distribution. GLM can be used to conduct analyses that parallel all the most commonly used parametric null hypothesis significance tests (NHST), such as <italic>t</italic>-tests, Anova, and linear regression. In other words, GLM can be used to test for differences between two or more samples and for relationships between two samples. GLM can also be used to do both at the same time, and it can handle more than two explanatory variables. The chapter then outlines the multiple model approach to analysis before introducing alternatives to a GLM within the GLZM framework.
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FRANSISKA OKTA SILVIA NINGRUM , 1017031029 · Digilib Repository Unila (Lampung University) · 2014
B.INGGRIS \nIn this study introduces explained on the estimation of parameters in a linear mixed model. Linear mixed model is one that contains both fixed effect and random effect. The aims of this study is going to discuss about the estimation and testing hypothesis for parameters
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Ningrum Fransiska Okta Silvia, 1017031029 · Digilib Repository Unila (Lampung University) · 2014
Pada penelitian ini dijelaskan m \ne \nngenai pendugaan parameter pada model linear \ncampuran \n(L \ni \nnear Mixed Model). \nModel Linear campuran merupakan \nmodel \nlinear yang terdapat lebih dari satu parameter, yaitu faktor tetap \n(fixed effect) \ndan \nfaktor acak \n(random effect) \n. \nPenelitian ini mengkaji tentang pendugaan parameter \ndan uji hipotesis pada model linear campuran \n. \nPada penelitian ini juga dilakukan \nsimulasi dengan menggunakan software SAS guna pendugaan parameter dan \nkuasa uji pada parameter \n훽 \ndan \n훾 \n. \nHasil dari simulasi menunjukkan bahwa \npenduga bagi parameter \n훽 \nmerupakan penduga yang \nover estimate \nsedangkan \npen \nduga bagi \n훾 \nmerupakan penduga yang \nbias walaupun perbedaan yang \ndi \nhasil \nkan \nnya tidak begitu jauh. \nHasil kuasa uji yang diperoleh menunjukkan \nbahwa \njika nilai \nβ \ndan \n훾 \nsemakin \nmenjauh dari H \n0, \nmaka kuasa ujinya pun \nsemakin besar atau peluang menolak H \n0 \ndimana H \n0 \nsalah \nsemakin besar. \nKata Kunci : model linear c \nampuran, \nfixed effect \n, \nrandom effect \n, \nsimulasi, \nkuasa u \nji.
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Somesh Das Gupta · 2000
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Anthony J. Culyer · 2014
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Nguyen, Rang Ngoc · Figshare · 2023
imt of femoral artery
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Natanael Spisak (12733857), Marc de Manuel (8039066), William Milligan (9593378) et al. · Figshare · 2024
<p>Until age <i>x</i><sub>0</sub> = 18 mutations accumulate at rate <i>μ</i><sub>0</sub> (in red) and after age <i>x</i><sub>0</sub> at rate <i>μ</i> (blue). (A) When <i>μ</i><sub>0</sub>><i>μ</i>, for data from donors of ages <i>x</i>><i>x</i><sub>0</sub>, a regression would yield a positive intercept, <i>b</i>>0. (B) When <i>μ</i><sub>0</sub>><i>μ</i>, such regression would yield a negative intercept, <i>b</i><0. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002678#sec008" target="_blank">discussion</a> in section “Relating model predictions to data” in the main text. Underlying data for this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002678#pbio.3002678.s008" target="_blank">S2 Data</a>.</p> <p>(TIFF)</p>
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Qingwen Li (1510687), Mengjiao Xu (6000998), Chuangchuang Pan (20833395) et al. · Figshare · 2025
<div> To address the environmental hazards caused by coal gangue waste, coal gangue concrete (CGC) has been proposed as a solution. However, due to its porosity and low strength, CGC faces numerous challenges in practical applications. To further improve its performance, fiber reinforced polymer (FRP) material was introduced to confine it. In this study, the PFC3D-FLAC3D coupling analysis method was employed to simulate the uniaxial compression test of FRP confined coal gangue concrete specimens. The influence of different FRP types (GFRP, CFRP, BFRP) and coal gangue replacement rates (0%, 50%, 100%) on the axial compression performance of coal gangue concrete columns was analyzed. Based on the indoor uniaxial compression test of glass fiber reinforced polymer (GFRP) confined coal gangue concrete, the modeling and calibration of coal gangue concrete columns confined by different FRP sheets were conducted. The strength variation and microstructure evolution mechanism of coal gangue concrete specimens confined by three kinds of FRP were discussed. The results indicated that the numerical model is highly accurate and consistent with existing experiments. The type of FRP significantly influences the confinement effect on coal gangue concrete specimens. As the coal gangue replacement rate increases, both the strength and elastic modulus of the specimens decrease. The difference of the spatial distribution of strong contact number and strong contact force reflect the microscopic manifestation of the macroscopic strength. The crack evolution of FRP confined coal gangue concrete went through three stages during uniaxial compression. This study is of great significance for selecting the appropriate type of FRP confinement for concrete under different coal gangue replacement rates. </div>
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Cardot, Herve, Ferraty, Frederic, Sarda, Pascal et al. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1998
Available from INIST (FR), Document Supply Service, under shelf-number : 22522, issue : a.1998 n.04 / INIST-CNRS - Institut de l'Information Scientifique et Technique
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Lauren M. Baker (16923489), Fabian C. Weigend (20497445), Krithika Swaminathan (11880541) et al. · Figshare · 2025
<div> Accurately estimating kinetic metrics, such as braking and propulsion forces, in real-world running environments enhances our understanding of performance, fatigue, and injury. Wearable inertial measurement units (IMUs) offer a potential solution to estimate kinetic metrics outside the lab when combined with machine learning. However, current IMU-based kinetic estimation models are trained and evaluated within a single environment, often on lab treadmills. The transferability of these treadmill-trained models during overground running in and out of the lab is underexplored, and the individualization and validation of such models remain a challenge. Toward bridging this gap, we trained a generalized model on treadmill data of 15 recreational runners and evaluated braking and propulsion force estimates during overground running in and out of the lab. We explored fine-tuning with individual data from lab-based overground running to quantify model performance improvements with individualization. The generalized and fine-tuned models were extrapolated to outdoor running for a subset of five participants, and estimates were compared to lab-based overground measurements. Evaluating the generalized model with a leave-one-out cross validation yielded overground braking and propulsion force root mean squared error of 4.3 ± 1.1 % bodyweight (%BW). Fine-tuning this model with eight strides reduced error to 2.6 ± 0.5 %BW. Outdoor force predictions from the fine-tuned model better aligned with expected linear trends between braking/propulsion impulses and speed than the generalized model. These results provide insights into the accuracy and applicability of IMU data-driven models for braking and propulsion estimation during overground running, facilitating the development of practical, individualized biomechanical analysis tools for real-world use. </div>
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Series on multivariate analysis · 2019
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Steven J. Janke, Frederick C. Tinsley · 2005
This chapter contains sections titled: Residual Plots Standardized Residuals Testing Assumption 1: Is X a Valid Predictor? Testing Assumption 2: Does E(ϵi) = 0 for all il Testing Assumption 2: Does Var(ϵi) = σ2 for all il Testing Assumption 3: Are the Errors Independent? Testing Assumption 4: Are the Errors Normal? Distribution of the Residuals Linear Algebra for Residuals (Optional) Exercises
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